Korean letter handwritten recognition using deep convolutional neural network on android platform
Sarah Purnamawati, Dian Rachmawati, Grace Ully Sira Lumanauw, Romi Fadillah Rahmat, Reza Taqyuddin · Journal of Physics Conference Series · 2018
Currently, popularity of Korean culture attracts many people to learn everything about Korea, particularly its language. To acquire Korean Language, every single learner needs to be able to understand Korean non-Latin character. A digital approach needs to be carried out in order to make Korean learning process easier. This study is done by using Deep Convolutional Neural Network (DCNN). DCNN performs the recognition process on the image based on the model that has been trained such as Inception-v3 Model. Subsequently, re-training process using transfer learning technique with the trained and re-trained value of model is carried though in order to develop a new model with a better performance without any specific systemic errors. The testing accuracy of this research results in 86,9%.